In this clustering phase, training ends when the loss has converged, as some have defined by the maximum percentage of images whose cluster assignments go unchanged between iterations [1]} or after a set number of iterations [2]}. In this work, we plot the loss across the iterations, observing when the loss has stabilized, as we found under simulation that some cluster assignments will go unchanged in consecutive iterations early on in the training despite the loss still decreasing at a steady rate. Once training ends, each image \(x_i\)  is assigned to the cluster with the highest probability of cluster assignment, that is, \(k^{\prime } = argmax_k(q_{ik})\) .
